Discovering a compound that will bind a protein therapeutic target with high affinity, while retaining favorable pharmacological properties, can be a time-consuming and expensive bottleneck in the drug-discovery process. Computational methods can speed this ligand discovery step, but are not yet reliable enough to circumvent extensive experimental testing, even in the favorable instance where the 3D structure of the protein is known. The central goal of this SBIR project is to develop and commercialize a new computational method for assessing the affinities of drug candidates for a protein target of known 3D structure, accounting efficiently for changes in translational, rotational and conformational entropy upon binding. This method will speed discovery of new medications to improve human health, and will generate substantial sales in the pharmaceutical and biotechnology industries, as well as from government and academic labs. During Phase I, we aim to demonstrate successful initial implementation of the method on a single computer and in parallel on a commodity compute cluster, and to show that it yields converged free energies in nontrivial test cases. We also aim to show that properly computed free energies can yield significantly different ligand rankings, relative to more elementary models.